SOTAVerified

Safe Exploration

Safe Exploration is an approach to collect ground truth data by safely interacting with the environment.

Source: Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1–50 of 135 papers

TitleStatusHype
SLAC: Simulation-Pretrained Latent Action Space for Whole-Body Real-World RL—0
Confidence-Guided Human-AI Collaboration: Reinforcement Learning with Distributional Proxy Value Propagation for Autonomous DrivingCode0
Bresa: Bio-inspired Reflexive Safe Reinforcement Learning for Contact-Rich Robotic Tasks—0
Safe exploration in reproducing kernel Hilbert spaces—0
Safety Representations for Safer Policy Learning—0
Learning to explore when mistakes are not allowed—0
ConRFT: A Reinforced Fine-tuning Method for VLA Models via Consistency PolicyCode3
Learning-Enhanced Safeguard Control for High-Relative-Degree Systems: Robust Optimization under Disturbances and Faults—0
Safe Bayesian Optimization for the Control of High-Dimensional Embodied Systems—0
ActSafe: Active Exploration with Safety Constraints for Reinforcement Learning—0
Robust Deep Reinforcement Learning for Volt-VAR Optimization in Active Distribution System under Uncertainty—0
Handling Long-Term Safety and Uncertainty in Safe Reinforcement LearningCode0
Revisiting Safe Exploration in Safe Reinforcement learning—0
A Safe Self-evolution Algorithm for Autonomous Driving Based on Data-Driven Risk Quantification Model—0
Meta SAC-Lag: Towards Deployable Safe Reinforcement Learning via MetaGradient-based Hyperparameter Tuning—0
A Safe Exploration Strategy for Model-free Task Adaptation in Safety-constrained Grid Environments—0
Exterior Penalty Policy Optimization with Penalty Metric Network under ConstraintsCode0
Transductive Active Learning with Application to Safe Bayesian OptimizationCode1
Enhanced Safety in Autonomous Driving: Integrating Latent State Diffusion Model for End-to-End Navigation—0
Highway Value Iteration Networks—0
Safe Reinforcement Learning in Black-Box Environments via Adaptive ShieldingCode0
Preparing for Black Swans: The Antifragility Imperative for Machine Learning—0
Contextual Affordances for Safe Exploration in Robotic Scenarios—0
Safe Exploration Using Bayesian World Models and Log-Barrier Optimization—0
Trajectory-wise Iterative Reinforcement Learning Framework for Auto-bidding—0
Information-Theoretic Safe Bayesian Optimization—0
A comparison of RL-based and PID controllers for 6-DOF swimming robots: hybrid underwater object trackingCode0
Towards Socially and Morally Aware RL agent: Reward Design With LLM—0
Towards Safe Load Balancing based on Control Barrier Functions and Deep Reinforcement Learning—0
Learning Human-like Representations to Enable Learning Human Values—0
Safe Exploration in Reinforcement Learning: Training Backup Control Barrier Functions with Zero Training Time Safety Violations—0
A safe exploration approach to constrained Markov decision processes—0
Safe Reinforcement Learning in a Simulated Robotic Arm—0
State-Wise Safe Reinforcement Learning With Pixel ObservationsCode1
Safe Exploration in Reinforcement Learning: A Generalized Formulation and Algorithms—0
Reinforcement Learning by Guided Safe Exploration—0
Probabilistic Counterexample Guidance for Safer Reinforcement Learning (Extended Version)Code0
Model-Assisted Probabilistic Safe Adaptive Control With Meta-Bayesian Learning—0
Safe Reinforcement Learning with Dead-Ends Avoidance and Recovery—0
Provably Learning Nash Policies in Constrained Markov Potential Games—0
Exploration of Unranked Items in Safe Online Learning to Re-Rank—0
System III: Learning with Domain Knowledge for Safety Constraints—0
Approximate Shielding of Atari Agents for Safe Exploration—0
Safe and Sample-efficient Reinforcement Learning for Clustered Dynamic EnvironmentsCode0
A Human-Centered Safe Robot Reinforcement Learning Framework with Interactive Behaviors—0
Information-Theoretic Safe Exploration with Gaussian ProcessesCode0
Benefits of Monotonicity in Safe Exploration with Gaussian ProcessesCode0
Atlas: Automate Online Service Configuration in Network SlicingCode0
The Pump Scheduling Problem: A Real-World Scenario for Reinforcement LearningCode0
Model-based Safe Deep Reinforcement Learning via a Constrained Proximal Policy Optimization AlgorithmCode1
Show:102550
← PrevPage 1 of 3Next →

No leaderboard results yet.